Job responsibilities
- Research and explore new machine learning methods through independent study, attending industry-leading conferences and experimentation
- Develop state-of-the art machine learning models to solve real-world problems and apply it to complex business critical problems in Cybersecurity, Software and Technology Infrastructure
- Collaborate with multiple partner teams in Cybersecurity, Software and Technology Infrastructure to deploy solutions into production
- Drive firmwide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business
- Contribute to reusable code and components that are shared internally and also ext
Required qualifications, capabilities and skills
- PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science. Or an MS with full time industry or research experience in the field.
- Hands-on experience and solid understanding of machine learning and deep learning methods
- Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
- Scientific thinking and the ability to invent
- Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
- Experience with big data and scalable model training
- Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
- Curious, hardworking and detail-oriented, and motivated by complex analytical problems
- Ability to work both independently and in highly collaborative team environments
Preferred qualifications, capabilities and skills
- Strong background in Mathematics and Statistics
- Familiarity with the financial services industries
- Experience with A/B experimentation and data/metric-driven product development
- Experience with cloud-native deployment in a large scale distributed environment
- Knowledge of large language models (LLMs) and accompanying toolsets the LLM ecosystem (e.g. Langchain, Vector databases, opensource Hugging Face Models)
- Knowledge in Reinforcement Learning or Meta Learning
- Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal
- Ability to develop and debug production-quality code
- Familiarity with continuous integration models and unit test development